Load all required libraries.

library(tidyverse)
## Warning: package 'tidyverse' was built under R version 3.6.3
## -- Attaching packages ---------------------------------------------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.2     v purrr   0.3.4
## v tibble  3.0.3     v dplyr   1.0.0
## v tidyr   1.1.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.5.0
## Warning: package 'ggplot2' was built under R version 3.6.3
## Warning: package 'tibble' was built under R version 3.6.3
## Warning: package 'readr' was built under R version 3.6.3
## Warning: package 'dplyr' was built under R version 3.6.3
## Warning: package 'forcats' was built under R version 3.6.3
## -- Conflicts ------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(plotly)
## Warning: package 'plotly' was built under R version 3.6.3
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(broom)
## Warning: package 'broom' was built under R version 3.6.3

Read in raw data from RDS.

raw_data <- readRDS("./n1_n2_cleaned_cases.rds")

Make a few small modifications to names and data for visualizations.

final_data <- raw_data %>% mutate(log_copy_per_L = log10(mean_copy_num_L)) %>%
  rename(Facility = wrf) %>%
  mutate(Facility = recode(Facility, 
                           "NO" = "WRF A",
                           "MI" = "WRF B",
                           "CC" = "WRF C"))

Seperate the data by gene target to ease layering in the final plot

#make three data layers
only_positives <<- subset(final_data, (!is.na(final_data$Facility)))
only_n1 <- subset(only_positives, target == "N1")
only_n2 <- subset(only_positives, target == "N2")
only_background <<-final_data %>% 
  select(c(date, cases_cum_clarke, new_cases_clarke, X7_day_ave_clarke, cases_per_100000_clarke)) %>%
  group_by(date) %>% summarise_if(is.numeric, mean)

#specify fun colors
background_color <- "#7570B3"
seven_day_ave_color <- "#E6AB02"
marker_colors <- c("N1" = '#1B9E77',"N2" ='#D95F02')
#remove facilty C for now
#only_n1 <- only_n1[!(only_n1$Facility == "WRF C"),]
#only_n2 <- only_n2[!(only_n2$Facility == "WRF C"),]

Build the main plot

      #first layer is the background epidemic curve
        p1 <- only_background %>%
              plotly::plot_ly() %>%
              plotly::add_trace(x = ~date, y = ~new_cases_clarke, 
                                type = "bar", 
                                hoverinfo = "text",
                                text = ~paste('</br> Date: ', date,
                                                     '</br> Daily Cases: ', new_cases_clarke),
                                alpha = 0.5,
                                name = "Daily Reported Cases",
                                color = background_color,
                                colors = background_color,
                                showlegend = FALSE) %>%
            layout(yaxis = list(title = "Clarke County Daily Cases", showline=TRUE)) %>%
            layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
        
        #renders the main plot layer two as seven day moving average
        p1 <- p1 %>% plotly::add_trace(x = ~date, y = ~X7_day_ave_clarke, 
                             type = "scatter",
                             mode = "lines",
                             hoverinfo = "text",
                            text = ~paste('</br> Date: ', date,
                                                     '</br> Seven-Day Moving Average: ', X7_day_ave_clarke),
                             name = "Seven Day Moving Average Athens",
                             line = list(color = seven_day_ave_color),
                             showlegend = FALSE)
      

        
        #renders the main plot layer three as positive target hits
        
        p2 <- plotly::plot_ly() %>%
          plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
                                       type = "scatter",
                                       mode = "markers",
                                       hoverinfo = "text",
                                       text = ~paste('</br> Date: ', date,
                                                     '</br> Facility: ', Facility,
                                                     '</br> Target: ', target,
                                                     '</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
                                       data = only_n1,
                                       symbol = ~Facility,
                                       marker = list(color = '#1B9E77', size = 8, opacity = 0.65),
                                       showlegend = FALSE) %>%
          plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
                                       type = "scatter",
                                       mode = "markers",
                                       hoverinfo = "text",
                                       text = ~paste('</br> Date: ', date,
                                                     '</br> Facility: ', Facility,
                                                     '</br> Target: ', target,
                                                     '</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
                                       data = only_n2,
                                       symbol = ~Facility,
                                       marker = list(color = '#D95F02', size = 8, opacity = 0.65),
                                       showlegend = FALSE) %>%
            layout(yaxis = list(title = "SARS CoV-2 Copies/L", 
                                 showline = TRUE,
                                 type = "log",
                                 dtick = 1,
                                 automargin = TRUE)) %>%
            layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
        
        #adds the limit of detection dashed line
        p2 <- p2 %>% plotly::add_segments(x = as.Date("2020-03-14"), 
                                          xend = ~max(date + 10), 
                                          y = 3571.429, yend = 3571.429,
                                          opacity = 0.35,
                                          line = list(color = "black", dash = "dash")) %>%
          layout(annotations = list(x = as.Date("2020-03-28"), y = 3.8, xref = "x", yref = "y", 
                                    text = "Limit of Detection", showarrow = FALSE))

        

        p1
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## Warning: Ignoring 1 observations
        p2
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Combine the two main plot pieces as a subplot

p_combined <-
    plotly::subplot(p2,p1, # plots to combine, top to bottom
      nrows = 2,
      heights = c(.6,.4),  # relative heights of the two plots
      shareX = TRUE,  # plots will share an X axis
      titleY = TRUE
    ) %>%
    # create a vertical "spike line" to compare data across 2 plots
    plotly::layout(
      xaxis = list(
        spikethickness = 1,
        spikedash = "dot",
        spikecolor = "black",
        spikemode = "across+marker",
        spikesnap = "cursor"
      ),
      yaxis = list(spikethickness = 0)
    )
## Warning: Ignoring 1 observations
p_combined

Save the plot to pull into the index

save(p_combined, file = "./plotly_fig.rda")

Save an htmlwidget for website embedding

htmlwidgets::saveWidget(p_combined, "plotly_fig.html")

Build loess smoothing figures figures

#create smoothing data frames 
#n1
smooth_n1 <- only_n1 %>% select(-c(Facility)) %>% 
  group_by(date, cases_cum_clarke, new_cases_clarke, X7_day_ave_clarke, cases_per_100000_clarke) %>%
  summarize(sum_copy_num_L = sum(mean_total_copies)) %>%
  ungroup() %>%
  mutate(log_sum_copies_L = log10(sum_copy_num_L)) %>%
  mutate(target = "N1")
## `summarise()` regrouping output by 'date', 'cases_cum_clarke', 'new_cases_clarke', 'X7_day_ave_clarke' (override with `.groups` argument)
#n2
smooth_n2 <- only_n2 %>% select(-c(Facility)) %>% 
  group_by(date, cases_cum_clarke, new_cases_clarke, X7_day_ave_clarke, cases_per_100000_clarke) %>%
  summarize(sum_copy_num_L = sum(mean_total_copies)) %>%
  ungroup() %>%
  mutate(log_sum_copies_L = log10(sum_copy_num_L)) %>%
  mutate(target = "N2")
## `summarise()` regrouping output by 'date', 'cases_cum_clarke', 'new_cases_clarke', 'X7_day_ave_clarke' (override with `.groups` argument)
#add trendlines 
#extract data from geom_smooth
#n1 extract
# *********************************span 0.6***********************************
#*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_n1 <- ggplot(smooth_n1, aes(x = date, y = log_sum_copies_L)) + 
  stat_smooth(aes(outfit=fit_n1<<-..y..), method = "loess", color = '#1B9E77', 
              span = 0.6, n = 134)
## Warning: Ignoring unknown aesthetics: outfit
#n2 extract
extract_n2 <- ggplot(smooth_n2, aes(x = date, y = log_sum_copies_L)) + 
  stat_smooth(aes(outfit=fit_n2<<-..y..), method = "loess", color = '#1B9E77', 
              span = 0.6, n = 134)
## Warning: Ignoring unknown aesthetics: outfit
#look at the fits to align dates and total observations
#n1
extract_n1
## `geom_smooth()` using formula 'y ~ x'

fit_n1
##   [1] 11.07337 11.23133 11.38495 11.53368 11.67693 11.81416 11.94480 12.06829
##   [9] 12.18474 12.29492 12.39927 12.49822 12.59221 12.68168 12.76705 12.84669
##  [17] 12.91899 12.98440 13.04338 13.09637 13.14384 13.18625 13.22404 13.25768
##  [25] 13.28762 13.31432 13.33822 13.35980 13.37950 13.38823 13.37842 13.35280
##  [33] 13.31410 13.26505 13.20838 13.14682 13.08310 13.01996 12.96012 12.90631
##  [41] 12.86127 12.82772 12.80839 12.79060 12.76303 12.73048 12.69771 12.66954
##  [49] 12.65073 12.64608 12.65363 12.66774 12.68770 12.71279 12.74229 12.77547
##  [57] 12.81162 12.85002 12.88994 12.93066 12.97148 13.01166 13.05048 13.08723
##  [65] 13.13082 13.18895 13.25910 13.33872 13.42528 13.51625 13.60909 13.70127
##  [73] 13.79025 13.87351 13.94849 14.01268 14.06354 14.09853 14.12572 14.15365
##  [81] 14.18047 14.20436 14.22346 14.23594 14.23997 14.23245 14.21265 14.18225
##  [89] 14.14294 14.09640 14.04433 13.98841 13.93032 13.87175 13.81439 13.75992
##  [97] 13.71004 13.66642 13.63075 13.59809 13.56277 13.52539 13.48654 13.44683
## [105] 13.40685 13.36719 13.32846 13.29124 13.25614 13.22376 13.19469 13.16952
## [113] 13.14886 13.12992 13.11009 13.09021 13.07111 13.05364 13.03864 13.02693
## [121] 13.01797 13.01062 13.00489 13.00080 12.99833 12.99750 12.99831 13.00077
## [129] 13.00488 13.01064 13.01807 13.02716 13.03791 13.05035
#n2
extract_n2
## `geom_smooth()` using formula 'y ~ x'

fit_n2
##   [1] 10.85262 11.04904 11.24008 11.42528 11.60418 11.77632 11.94124 12.09848
##   [9] 12.24811 12.39074 12.52675 12.65647 12.78028 12.89854 13.01160 13.11816
##  [17] 13.21693 13.30828 13.39257 13.47016 13.54141 13.60669 13.66637 13.72079
##  [25] 13.77034 13.81537 13.85625 13.89333 13.92699 13.94907 13.95282 13.94070
##  [33] 13.91517 13.87869 13.83373 13.78275 13.72821 13.67257 13.61830 13.56786
##  [41] 13.52370 13.48830 13.46411 13.43359 13.38313 13.32109 13.25586 13.19579
##  [49] 13.14927 13.12467 13.11573 13.11046 13.10862 13.10998 13.11428 13.12130
##  [57] 13.13078 13.14249 13.15619 13.17164 13.18859 13.20681 13.22605 13.24608
##  [65] 13.27364 13.31417 13.36549 13.42538 13.49164 13.56207 13.63446 13.70661
##  [73] 13.77633 13.84140 13.89962 13.94879 13.98672 14.01118 14.02748 14.04219
##  [81] 14.05506 14.06584 14.07429 14.08016 14.08320 14.08030 14.06929 14.05126
##  [89] 14.02731 13.99856 13.96611 13.93107 13.89454 13.85762 13.82143 13.78706
##  [97] 13.75563 13.72824 13.70599 13.68558 13.66326 13.63942 13.61451 13.58892
## [105] 13.56309 13.53742 13.51234 13.48827 13.46562 13.44481 13.42626 13.41038
## [113] 13.39761 13.38617 13.37437 13.36269 13.35160 13.34159 13.33313 13.32670
## [121] 13.32198 13.31831 13.31569 13.31411 13.31357 13.31407 13.31560 13.31816
## [129] 13.32175 13.32637 13.33201 13.33867 13.34634 13.35503
#assign fits to a vector
n1_trend <- fit_n1
n2_trend <- fit_n2

#extract y min and max for each
limits_n1 <- ggplot_build(extract_n1)$data
## `geom_smooth()` using formula 'y ~ x'
limits_n1 <- as.data.frame(limits_n1)
n1_ymin <- limits_n1$ymin
n1_ymax <- limits_n1$ymax

limits_n2 <- ggplot_build(extract_n2)$data
## `geom_smooth()` using formula 'y ~ x'
limits_n2 <- as.data.frame(limits_n2)
n2_ymin <- limits_n2$ymin
n2_ymax <- limits_n2$ymax

#reassign dataframes (just to be safe)
work_n1 <- smooth_n1
work_n2 <- smooth_n2

#fill in missing dates to smooth fits
work_n1 <- work_n1 %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_n1 <- work_n1$date
work_n2 <- work_n2 %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_n2 <- work_n2$date

#create a new smooth dataframe to layer
smooth_frame_n1 <- data.frame(date_vec_n1, n1_trend, n1_ymin, n1_ymax)
smooth_frame_n2 <- data.frame(date_vec_n2, n2_trend, n2_ymin, n2_ymax)
#make plotlys

#plot smooth frames
p3 <- plotly::plot_ly() %>%
  plotly::add_lines(x = ~date_vec_n1, y = ~n1_trend,
                    data = smooth_frame_n1,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_n1,
                                  '</br> Median Log Copies: ', round(n1_trend, digits = 2),
                                  '</br> Target: N1'),
                    line = list(color = '#1B9E77', size = 8, opacity = 0.65),
                    showlegend = FALSE) %>%
plotly::add_lines(x = ~date_vec_n2, y = ~n2_trend,
                  data = smooth_frame_n2,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_n2,
                                  '</br> Median Log Copies: ', round(n2_trend, digits = 2),
                                  '</br> Target: N2'),
                    line = list(color = '#D95F02', size = 8, opacity = 0.65),
                    showlegend = FALSE) %>%
plotly::add_ribbons(x ~date_vec_n1, ymin = ~n1_ymin, ymax = ~n1_ymax,
                    showlegend = FALSE,
                    opacity = 0.25,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_n1, #leaving in case we want to change
                                  '</br> Max Log Copies: ', round(n1_ymax, digits = 2),
                                  '</br> Min Log Copies: ', round(n1_ymin, digits = 2),
                                  '</br> Target: N1'),
                    name = "",
                    line = list(color = '#1B9E77')) %>%
plotly::add_ribbons(x ~date_vec_n2, ymin = ~n2_ymin, ymax = ~n2_ymax,
                    showlegend = FALSE,
                    opacity = 0.25,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_n2, #leaving in case we want to change
                                  '</br> Max Log Copies: ', round(n2_ymax, digits = 2),
                                  '</br> Min Log Copies: ', round(n2_ymin, digits = 2),
                                  '</br> Target: N2'),
                    name = "",
                    line = list(color = '#D95F02')) %>%
                layout(yaxis = list(title = "Total Log SARS CoV-2 Copies", 
                                 showline = TRUE,
                                 automargin = TRUE)) %>%
                layout(xaxis = list(title = "Date")) %>%
    plotly::add_segments(x = as.Date("2020-06-24"), 
                                          xend = as.Date("2020-06-24"), 
                                          y = ~min(n1_ymin), yend = ~max(n1_ymax),
                                          opacity = 0.35,
                                          name = "Bars Repoen",
                                          hoverinfo = "text",
                                          text = "</br> Bars Reopen",
                                                 "</br> 2020-06-24",
                                          showlegend = FALSE,
                                          line = list(color = "black", dash = "dash")) %>%
    plotly::add_segments(x = as.Date("2020-07-09"), 
                                          xend = as.Date("2020-07-09"), 
                                          y = ~min(n1_ymin), yend = ~max(n1_ymax),
                                          opacity = 0.35,
                                          name = "Mask Mandate",
                                          hoverinfo = "text",
                                          text = "</br> Mask Mandate",
                                                 "</br> 2020-07-09",
                                          showlegend = FALSE,
                                          line = list(color = "black", dash = "dash")) %>%
    plotly::add_segments(x = as.Date("2020-08-20"), 
                                          xend = as.Date("2020-08-20"), 
                                          y = ~min(n1_ymin), yend = ~max(n1_ymax),
                                          opacity = 0.35,
                                          name = "</br> Classes Begin",
                                                 "</br> 2020-08-20",
                                          hoverinfo = "text",
                                          text = "Classes Begin",
                                          showlegend = FALSE,
                                          line = list(color = "black", dash = "dash")) %>%
    plotly::add_segments(x = as.Date("2020-10-03"), 
                                          xend = as.Date("2020-10-03"), 
                                          y = ~min(n1_ymin), yend = ~max(n1_ymax),
                                          opacity = 0.35,
                                          name = "</br> First Home Football Game",
                                                 "</br> 2020-10-03",
                                          hoverinfo = "text",
                                          text = "First Home Football Game",
                                          showlegend = FALSE,
                                          line = list(color = "black", dash = "dash")) %>%
  plotly::add_markers(x = ~date, y = ~log_sum_copies_L,
                      data = smooth_n1,
                       hoverinfo = "text",
                       showlegend = FALSE,
                       text = ~paste('</br> Date: ', date, 
                                     '</br> Observed Log Copies: ', round(log_sum_copies_L, digits = 2)),
                       marker = list(color = '#1B9E77', size = 6, opacity = 0.65)) %>%
    plotly::add_markers(x = ~date, y = ~log_sum_copies_L,
                      data = smooth_n2,
                       hoverinfo = "text",
                       showlegend = FALSE,
                       text = ~paste('</br> Date: ', date, 
                                     '</br> Observed Log Copies: ', round(log_sum_copies_L, digits = 2)),
                       marker = list(color = '#D95F02', size = 6, opacity = 0.65))

p3

Create final trend plot by stacking with epidemic curve

smooth_extract <-
    plotly::subplot(p3,p1, # plots to combine, top to bottom
      nrows = 2,
      heights = c(.6,.4),  # relative heights of the two plots
      shareX = TRUE,  # plots will share an X axis
      titleY = TRUE
    ) %>%
    # create a vertical "spike line" to compare data across 2 plots
    plotly::layout(
      xaxis = list(
        spikethickness = 1,
        spikedash = "dot",
        spikecolor = "black",
        spikemode = "across+marker",
        spikesnap = "cursor"
      ),
      yaxis = list(spikethickness = 0)
    )
## Warning: Ignoring 1 observations
smooth_extract
save(smooth_extract, file = "./smooth_extract.rda")